Dianchao Lin

Fangfang Zheng, Saif Eddin Jabari, Henry X. Liu et al.

This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific speed-spacing relations, i.e., parametric uncertainty. It also results in smooth vehicle trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby, overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. We utilize ensemble filtering techniques for data assimilation (traffic state estimation), but derive the mean and covariance dynamics as the ensemble sizes go to infinity, thereby bypassing the need to sample from the parameter distributions while estimating the traffic states. As a result, the estimation algorithm is just a standard Kalman-Bucy algorithm, which renders the proposed approach amenable to real-time applications using recursive data. Data assimilation examples are performed and our results indicate good agreement with out-of-sample data.

Jie Fang, Xiongwei Wu, Dianchao Lin et al.

The growing use of probe vehicles generates a huge number of GNSS data. Limited by the satellite positioning technology, further improving the accuracy of map-matching is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle's spatial-temporal information of the present trip is the most useful with the least amount of data. In addition, there are a large amount of other data, e.g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most map-matching studies only used the ego vehicle's data and ignored other vehicles' data. Based on it, this paper designs a new map-matching method to make full use of "Big data". We first sort all data into four groups according to their spatial and temporal distance from the present matching probe which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, a score for historical usage, and a score for traffic state using the spectral graph Markov neutral network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance map-matching accuracy. Furthermore, our method outperforms the others, especially when GNSS probing frequency is less than 0.01 Hz.

Saif Eddin Jabari, Deepthi Mary Dilip, DianChao Lin et al.

This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measurements using an efficient message passing technique. The approach ensures that estimated speeds and traffic densities are non-negative with probability one. The estimation technique is tested using vehicle trajectory datasets generated using an independent microscopic traffic simulator and is shown to efficiently reproduce traffic conditions with probe vehicle penetration levels as little as 10\%. The proposed algorithm is also compared with state-of-the-art traffic state estimation techniques developed for the same purpose and it is shown that the proposed approach can outperform the state-of-the-art techniques in terms reconstruction accuracy.